Measuring and Improving Colonoscopy Quality Using Natural Language Processing

描述(申请人提供)：使用自然语言处理测量和改进结肠镜检查质量。在美国，结肠镜检查是筛查结直肠癌的主要方法。然而，它在筛查方面的有效性受到性能差异的限制。例如，医生在结肠镜检查中发现被称为腺瘤的癌症前体的比率已经被证明在不同的医生之间存在三倍的差异。结肠镜检查是由腺瘤发现率低的医生进行的，患者继发结直肠癌的风险更高。我们的建议以测量、理解和提高结肠镜检查质量为中心。本工作的主要创新点是使用自然语言处理(NLP)来衡量结肠镜检查的质量。NLP是计算机科学的一个领域，在这个领域中，计算机被训练来“阅读”文本以识别相关数据。我们开发并验证了第一个基于NLP的计算机软件应用程序(C-QUAL)，它分析结肠镜检查和相关的病理报告。我们的主要质量指标是腺瘤发现率，因为它是一种常见的、经过验证的指标，与结直肠癌发病率有关。然而，我们使用了许多次要质量衡量标准。我们将C-QUAL应用于一个医疗系统中的25,000多份结肠镜检查报告，发现医生在质量测量方面的表现存在很大差异。在之前工作的基础上，我们的目标是使用C-QUAL来测量美国一系列实践环境中的结肠镜检查质量，了解是什么驱动了结肠镜检查质量的差异，并提高了结肠镜检查质量。在目标1中，我们建议使用C-QUAL工具来衡量4个不同医疗保健系统的绩效。这将是对腺瘤发现率差异的最大评估之一，将跨越不同的地理区域、支付系统和执业环境。在目标2中，我们试图了解为什么质量会有差异。我们将调查4个医疗保健系统的提供者，了解可能影响质量的因素。我们将把这些调查结果与目标1中评估的腺瘤发现率联系起来，寻找关键的联系。在目标3中，我们将使用一种新的反馈方法来提高质量。我们将在一个医疗保健系统中随机对医生进行两种不同类型的反馈，并在两年内跟踪他们的质量改善情况。我们的建议是第一次使用这种创新的方法来衡量结肠镜检查的质量，并使用质量评分来减少结肠镜检查性能的差异。总而言之，这三个目标的结果与NCI对提高结直肠癌筛查质量的关注是一致的。

DESCRIPTION (provided by applicant): Measuring and Improving Colonoscopy Quality Using Natural Language Processing. Colonoscopy is the predominant method for screening for colorectal cancer in the US. Yet, its effectiveness in screening is limited by variation in performance. For example, the rate at which physicians detect cancer precursors called adenomas during a colonoscopy has been shown to vary three-fold from one physician to another. A patient whose colonoscopy is performed by a physician with a low adenoma detection rate has a higher risk of subsequent colorectal cancer. Our proposal centers on measuring, understanding, and improving colonoscopy quality. The major innovation of this work is to use natural language processing (NLP) to measure the quality of colonoscopy. NLP is a field of computer science in which a computer is trained to "read" text to identify relevant data We developed and validated the first NLP-based computer software application (C-QUAL) that analyzes colonoscopy and associated pathology reports. Our primary quality measure is adenoma detection rate because it is a common, validated measure that is linked to colorectal cancer incidence. However, we use a number of secondary quality measures. We applied C-QUAL to over 25,000 colonoscopy reports in one health system and found large variation in physician's performance on the quality measures. Building on this prior work, our goal is to use C-QUAL to measure colonoscopy quality across a spectrum of US practice environments, to understand what drives variation in colonoscopy quality, and to improve colonoscopy quality. In Aim 1, we propose to use the C-QUAL tool to measure performance in 4 diverse health care systems. This will be one of the largest assessments of the variation in adenoma detection rates and will span different geographic regions, payment systems, and practice settings. In Aim 2, we seek to understand why there is variation in quality. We will survey providers at the 4 health care systems about factors that might affect quality. We will link those survey results to the adenoma detection rates assessed in Aim 1 and look for key associations. In Aim 3, we will use a novel feedback method to improve quality. We will randomize physicians in one health care system to two different types of feedback and track their quality improvement over a two-year period. Our proposal is the first to use this innovative method to measure colonoscopy quality and to use the quality scores to decrease the variation in colonoscopy performance. Together the results of the 3 aims are consistent with the NCI's focus on improving the quality of colorectal cancer screening.